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def unilm_mask(inputs, s):
    idxs = torch.cumsum(s, dim=1)
    mask = idxs[:, None, :] <= idxs[:, :, None]
    mask = mask[:, None].squeeze(1)
    return mask.to(dtype=torch.int64)
def create_lm_mask(attention_mask, direction='l2r'):

    seq_len = attention_mask.size(-1)
    if attention_mask.ndim == 2:
        attention_mask = attention_mask.view(-1, 1, seq_len)

napoler

>>> x = torch.randn(3, 2)
>>> y = torch.ones(3, 2)
>>> x
tensor([[-0.4620,  0.3139],
        [ 0.3898, -0.7197],
        [ 0.0478, -0.1657]])
>>> torch.where(x > 0, x, y)
tensor([[ 1.0000,  0.3139],
        [ 0.3898,  1.0000],
        [ 0.0478,  1.0000]])

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optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=0.1)

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model = Model()
# 这里是一般情况，共享层往往不止一层，所以做一个for循环
for para in model.linear1.parameters():
 para.requires_grad = False
# 假如真的只有一层也可以这样操作：
# model.linear1.weight.requires_grad = False

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# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever

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function throttle( fn, time ) {
    var t = 0;
    return function() {
        var args = arguments, ctx = this;
        clearTimeout(t);

        t = setTimeout( function() {
            fn.apply( ctx, args );
        }, time );
    };

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        self.config = AutoConfig.from_pretrained(MODEL_NAME)
        # tokenizer = BertTokenizer.from_pretrained(tokenizer_MODEL_NAME)

        self.model = AutoModel.from_pretrained(MODEL_NAME)

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import torch.nn as nn
import torch


class ConvLSTMCell(nn.Module):

    def __init__(self, input_dim, hidden_dim, kernel_size, bias):
        """
        Initialize ConvLSTM cell.

napoler

import torch
import torch.nn as nn

from models.ConvLSTMCell import ConvLSTMCell

class EncoderDecoderConvLSTM(nn.Module):
    def __init__(self, nf, in_chan):
        super(EncoderDecoderConvLSTM, self).__init__()

        """ ARCHITECTURE 

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